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Working with Data Flow Task in SSIS Packages

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These are the SSIS practicals in MSBI

  1. Architecture
  2. Execution
  3. Data flow task operations
  4. Sources
  5. Destinations
  6. Transformations

SSIS Practical Architecture

Solution (collection of projects) –> Project (collection of packages) –>Package (A discrete unit of work for doing ETL operations & Administrative tasks)

  • Control flow (Tasks, containers, plan tasks, maintenance)
  • Data flow (Sources, Destinations, Transformations)
  • Package explorer
  • Event Handling

Start àPrograms  –> SQL server 2008 –> click BIDSà File –> New –> Project –> Select ‘integration services project’ in templates window –> Enter name –> click ok

Project Name: Test_Solution_Project

Location     : E:/Test


By default there is no solution is presented, so we can create a solution at the project creation time.

–> Click view menu –> Solution explorer (It describes the projects, packages, data source views, data source information)


[It is used to connect to the DB it can be reusable “ACROSS packages”]

Data source view:

It is the logical object for the physical collection of tables or view in data sources.

Connection manager:

For every connection we can take a name, name can be reusable within the program (or) package.

In case of flat file:  Folder the file name taken as connection string for connection  manager.

In case of relation, source:  Server name and data base name taken as part of connection string in connection manager.

Various ways of package execution:

a) By pressing “F5”

b) Solution explorer –> packages –> Right click –> Execute package

c) Debug menu –> Start debugging

d) SSMS –> Integration services –> MSDB –> package –> rtclick –> run package

e) By using DTUTIL, DTUTIL Exec.facilities …etc

Colors and their meanings:

White –> Ready to execute

Yellow –> Running

Red –> Fail

Green –> Success

Grey –> disable

Ensuring Package success, Failure, Errors, Bottlenecks:

We observe this information in “Progress tab” or “Log Providers”

(a) Progress tab information: It describes how the package validated and executed step by step from starting to ending. Generally it displays

  • The no. of rows operated
  • Source and destination connections
  • The amount of time taken b/w one statement to another statement etc

 (b) Log Providers: discussed later in this book


It is the value which is changeable within the package. There are two types of variables they are

1) System defined variables: These variables hold system information.

These variables store under SYSTEM

Syntax:SYSTEM: : < variable Name>


              SYSTEM: : Execution ID

2) User Defined Variable: These variables are created by the user only. These variables store under user name space

Syntax:   USER : : i

USER : : Name Var

USER : : Temp_date ..etc

MindMajix Youtube Channel

Navigation:SSIS Menuàvariablesà in the variables window click the top most left corner option to create new variable.


Name                 Scope               Data                 Type Value

   l                       Package           Int 32                          1


Variable scope: The extent we use the variable is called the scope of variable. There are 2 scopes

a) Package Level: Within the package anywhere we can use this variable

b) Task Level: Within the task only we can use the variable.

Working with Data Flow Task

To move the data from source to destination and to perform intermediate operations this task is mandatory. Frequently used sources are flat file source and OLE DB source and frequently used destinations are flat file destination and OLE DB destination.

Real time modes of flat file sources:

123 VINAY                                                   123 VINAY

456 SIVAM                                                  25 KISHORE

Fixed width                                                  Delimited (separated or Variable text)

ID –> 3 chars                                              ID à Max 10 char

Name –> 5 char                                         Name à Max 30 char

Row Delimiter:- ‘|’(pipe)

Column Delimiter:- ,(comma)

Row Delimiter:- CRLF(enter char)      [carriage return line or forward]

Column Delimiter:- No

Eg:- Moving data from file to file         (comma delimited)

Sol:- 1. Take data flow task on control flow

2. Go to data flow task à drag and drop flat file source & destination and do the below settings.

INPUT: Party_3RC –> notepad

File name: Party_3RC

No of rows: 13


–> Connection Manager Name is reusable in the packages.

IS Project 1:- Microsoft visual studio.

IS Project 1

FLAT FILE Source:-                                                                           FLAT FILE Destination:-

Select –> rtclick –> Edit                                                                  select –> rtclick –> Edit

Flat file connection manager                                                         flat file connection manager –>

Click New                                                                                      click new –> select Delimited –> click ok

Connection manager name: SRC                                                   Connection manager name: TGT

File name: Browse to the i/p folder                                                File name: Browse to the o/p folder

Party_SRC.Txt file.                                                                        and specify party_output.txt

Format: Delimited                                                                            Format: Delimited

Header Row Delimited: {CR}-{LF}                                                  Header Row Delimited: {CR}-{LF}

Header Row to skip: 1

Column names in the first data row:                                               Column names in the first data row:

Check it                                                                                            Check it

Click preview –> click ok –> ok                                        Click preview –> click ok –> ok, Go to mappings –> check whether required target columns –                                                                                                                     -> ok.

After assigning both source and destination


OLE DB:- Object Linking and embedding Data Base

Eg:- Moving data from table to table from source data base

DB_MSBI to MSBI_DB destination data base

Navigation: open BIDS à take data flow task in control flow

OLEDB source  —-> OLEDB Destination

OLEDB –> Object linking and embedding data base –> Universal provider to any data base or application (excel..etc)

OLEDB source –> Rt click –> edit –>

OLEDB connection manager –> New –> New

Provider: native OLEDB Native SQL server client 10.0

Server name: local host

Select Authentication: windows (or) SQL server.

Select database: DB_MSBI

Click test connection –> ok –> ok –> ok

Name of the table or view –> emp –>ok –> ok

OLEDB Destination –> Rtclick –> edit

OLEDB connection manager –> New –>New

Provider: native OLEDB native SQL server client 10.0

Server name: local host

Select Authentication: windows (or) SQL server.

Select database: DB_VINAY

Click test connection –> ok –> ok –>ok

Name of the table or view –> click new –> change table name –>ok

Go mapping –> connect required source column to required from one work sheet in another excel

Eg: moving data from flat file to Raw file.

Raw file contains binary information, so we are not able to read.


Flat File

–> raw file destination –> edit –> connection manager –> rtclick

Access mode –> file name

File name –> file path

Write always –> create always

Columns –> select columns –> ok

Eg: loading data from XML file to table in SQL server database.






< /Student 1>

< Student 1>



< /Student 1>

XML source –> rtclick –> edit –> data access mode –> XML location

OLEDB dest –> Generate XSD –>Ok.

Note: XML schema definition must be specified (or) generated to the corresponding XML file.


Frequently Asked MSBI Interview Questions & Answers



These are intermediate operations performed between source and destination.

Eg: concatenation, addition, sorting, merging etc.

There are different data flow transformations provided for different operations.

Sort transformation:

It sorts the data in the specified order (ascending or descending)

It has some flexibility to do sorting on multiple columns (by giving sort order)

There is a feature called “remove rows with duplicates” which helps us to display unique rows in sorting by eliminating duplicates.

Eg: display the data by location in ascending order, with in the location names in descending order.


Sort transformation

> Rc –> EDIT Available input columns  –> select columns

  1.   Parityloc
  2.   Partyname


input columns  –> select columns

Derived column transformation:

It performs operation row by row. It does different calculations, aggregation, concatenation, transformations, conversions etc for the columns in the rows.


a)      Display name and location by concatenation.

b)      Display income, if it is null 99999.

c)       Display income increment by 12%.

d)      Display a new field with current date as business date.

e)      Display default company code as 21000.


Derived Column Transformation


I.            When we retrieve the data from flat file, all the columns belong to string data type (DT-STR).

II.           When we retrieve the data from excel sheet, all the file it’s belong to (DT-WSTR).

Data conversions:

                Data conversions are done in three ways.

a)      By using data conversion transformation.

b)      By using type cast operator in expression.


(column name).

(DT-I4) (PartyIncome)

(DT-DBDATE) (‘2010-10-10’)

c)       Directly doing at flat file source itself.

Rt click on flat file source –> show advanced editor –> input and output properties –> flat file source O/P –> output columns –> party income –> data type properties –> data type  →four-byte signed integer(DT-I4)

Aggregate Transformation:

It performs aggregate operations such as average, sum, count, min, max, count distinct, group by

  • If the field or expression is of numeric data type we can perform the above all the operations.
  • If the field is string or data, we perform limited operations group, count, distinct count etc.

Eg: display location wise income sum and average.


Aggregate Transformation


I/P Column

Flat file source addition properties:

àRetain null values from the source as null values. It helps us to treat nulls coming from the source as nulls only.  (if we uncheck this option null treated as zero for integers, space for string)

Error output options:

In case of error or truncated values coming from source we can use either of the below options.

a)      Ignore failure → in case of error or truncations it ignores the failure.

b)      Redirect row → in case of error or truncation it redirect the row to the another destination.

c)       Fail component → in case of error or truncation it simply fails the component.


Flat file source àrtclickàeditàerror O/P.

Flat file destination files format:

  1. Delimited àthe columns are delimited by commas, expect the last one which is delimited by the new line character.
  2. Fixed widthàthe columns are defined by fixed widths.
  3. Fixed width with row delimiters àthe columns are defined by fixed width. An extra column, delimited by the new line characters is added to define row delimiters.
  4. Ragged right àthe columns are defined by fixed width, expect the last one which is delimited by the new line character.

OLEDB Source properties:

OLEDB Source àrtclickàeditàdata

Access mode:

  • Table or view
  • Table name or view name variable
  • SQL command
  • SQL command from variable

Note:  we use variables for dynamic source retrieval.

Multicast Transformation:           

It creates multiple copies of some source. So that instead of doing multiple operations on the same source in multiple package we can do in a single package by using multicast.

→ It improves the performance because it reads the data only one time from the source.

Package-1                                                           package-2                                                           package-3

EMP                                                                        EMP                                                                     EMP


Multicast Transformation

  1. Three buffers for source
  2. Multiple read on source

Advantages of single package split


Single package split

  1. Source read only one time
  2. Single time buffer occupation

Merges transformation:

It Merges multiple input data source here the restriction is the sources should be the sorted order so that the output will also be in the sorted order.

There are two ways to implement

If the source are not in sorted order do the below process


Merges transformation


Conditional split transformation:

a) It splits the data based on the condition.

b) There are two types of output comes from this transformation.

Conditions matched output.

Conditions unmatched output or default output.

Eg: move HYD, Bang, data to separate files and unmatched data to another file.


Conditional split transformation

→ Conditional split → rt clickà


1)                    HYD-DATA            [PARTYLOC]=”HYD”

2)                    BLORE-DATA       [PARTYLOC]=”BLORE”


Default output name → Unmatched-data → Ok

Union all transformation:

  • It merges multiple input sources (two or more)
  • No need to take the input in the sorted order, so that the output will also have unsorted data.


Union all transformation


1) Input sources structures should be same. [No.of columns, order of data type of columns]

OLEDB Source Editor Properties

Data access mode:

  1. Table or view → to retrieve the data from table or view
  2. Table name or view name variable → table name or view name takes from the table
  3. SQL command → we write a customized query to retrieve the data from the object, so that required rows and column retrieved and occupies less buffer every time. Eg:  select Party ID, Party Name from Party where Party code in (20, 40, 60)
  4. SQL command from variable → we pass SQL command from a variable.

Note: this variable generally recommended at the time of dynamic retrieval of data.

OLEDB Destination Additional properties

Data access mode:

i) Table or view

ii) Table or view-fast load → it loads the data very fast compared to normal view or table loading.

During the fast load there are couples of options we must select according to the situation.

  • Keep identity
  • Keep nulls
  • Table Lock
  • Check Constraints

Rows per batch 1000

Keep identify uses identity columns generated values.

Note: This fast load option is useful when the table is having clustered index.

iii) Table name or view name-fast load

iv) SQL command.

Question: In data accessing mode after selecting table or view option and clicking new button to create a table, then when does the table will be created.

Sol: After writing changes

Immediately table created (but not at execution time)

SSIS Expressions

1) Write expression when they are small because too many expressions and complex expressions decrease the performance.

2) Generally we use expressions in various places.

  • Precedence constraints
  • Variables
  • For loop
  • Connection string in the connection manager
  • Derived column transformation
  • Conditional split

As part of expressions there are many functions, typecasts and operators available.

Mathematical function: [Mathematical operations on numerical values]


Eg: Round (4.82) → 5           ABS (4.82) → 5      ceiling (4.82) → 5

Round (4.26) → 4          ABS (-3.92) →4      floor (4.82) → 4

String Function: [Manipulates string columns/expressions.]


Eg: LOWER (“ABC”) →abc

LTRIM (“abc”) → abc

–> It removes the space.

TRIM(“abc”) →abc

REVERSE (“abc”) → cba

FIND STRING (“VINNNAY”,”N”, 3) → 5               [3rd position]

REPLICATE (“a”, 3) → aaa

REPLACE (“VINAY”, ”Na”, “NNa”)



2 point string length 2 → ”IN”


Date/Time function: [To work with day, month, year etc.. In date expressions]


DAY (DT-DATE) “2011-09-04” –> 04

MONTH (DT-DATE) “2010-09-04” –> 09

DATE ADD (“MONTH”, 4, (DT-DATE) “2010-05-04”) –> 2010-09-04.


DATE DIFF (“MONTH”, (DT-DATE)”2011-07-09”, (DT-DATE)”2011-04-09”)


“DAY”            →     90 days

“YEAR”         →     0 years

Null functions: [validate null argument]

a)      Is null (expression) –> result/true or false

b)      Null (DT-DATE) –> null [useful to display “null date” value]

Type cast: [convert one data type of other data type.]

  • DT-I4 (column or expression)
  • (DT-STR, “length”, “codepage”) (column or expression)
  • (DT-WSTR(Wide), “length”) (col. or exp.)
  • (DT-numeric, precision, scale) (“)

Eg: (DT-Numeric,2,6) (123456)à1234.56.


&&———–logical AND

||————logical OR

?: –> writing if condition


Eg: is null (party name)? “unknown:” TRIM (party name)

Display expression if date is null or the date length is zero or the date is having null date, display null otherwise display date.

IS NULL (JDATE)||len (trim((DT-WSTR,10)JDATE))==0||JDATE==”00-00-0000”? NULL(DT-DATE):JDATE

List: ADD, concatenates (+), subtract, Negate, Multiply, Divide, modulo (%), parentheses ([]), equal (==), Unequal (!=), Greater (>)..etc

  • Display an expression where the file name should be appeared with the current time stamp in this format.


CDATEà”2011-08-09 04:23:22:00000

“File Name-“+

Substring ((DT-WSTR, 30) CDATE, 1, 10) +”-“+

Substring ((DT-WSTR, 30) CDATE, 12, 2) +

Substring ((DT-WSTR, 30) CDATE, 15, 2) +

Substring ((DT-WSTR, 30) CDATE, 18, 2) +”.TXT


Fast load option is useful when the table is having clustered index.

In case sorted input sources:


Input sources

0 –> Un sorted

1 –> Sorted

  • Take 2 flat files and assign locations
  • Go to each flat file –> Rt click –> show advanced editor
  • Input and output properties –> flat file source output
  • Is sorted: true

Output columns –> party ID –> data types: change DT-I4 sort key position:1

Flat file source error output –> is sorted: true

Output column –> flat file source error output column sort key option: 1 ok –>ok

Note:  merge works with two source at a time


Different between merge and union all

Merge                                                                                  Union all

Only two sources                                                             No limit

Input should be sorted                                                  Not applicable

Sorted result                                                                      Not applicable


Merge join:

It performs merge operation along with joins. Generally it supports the below joins.

a)      Inner join

b)      Left join

c)       Full join


Emp table


10 IT ?

40 HR ?

Dept Table


1 A 10

2 B 20


Query structure:

Select cols/* from cross join no where condition.

Inner join


Left join


Right join < Table B >ON

Full outer join


Eg: select E.EID, E.ENAME, E.EID, D.DID, D.DNAME from EMP E

Cross join dept D

                Inner join dept D ON E.EID=D.DID

Left outer join dept D ON E.EID=D.DID

                Right outer join dept D ON E.EID=D.DID

Full outer join dept D ON E.EID=D.DID




Cache creation:

OLEDB source [Dept Table]


Cache transformation  –> RC –> edit –> cache connection manager new  –> connection mgr name; Dept cache

  • Use file cache

Browse –> Output folder –> DBP _cache .CAW –> columns –> DEPT ID –> index position –> ok –>  ok –> run(Package and see cache generate)


Cache creation

–> Merge join –> rt click  –> merge join transformation editor –> join type: left outer join


left outer join

Look up transformation:

It look up the required values on target and fetches relevant result. [Exact result]

Real time usage:

  • To fetch relevant value
  • While working with SCDs [slowly changing dimension]
  • To have exact match with destination and to improve query retrieval fast (it uses caches)

Types of caches: 

  • Full cache
  • No cache
  • Partial cache

No cache:

 Here there is no cache to the target table. So every time source query hits the database and fetches the result.


   If the source data changing frequently and less no.of records are there it is recommended.


No cache

Draw backs:

1. Hits on the target increases and traffic also high.

Full cache: Here there is a cache for the target table, so every source request goes to the cache and fetches the data.  


Full cache


If the target is not changed and having more records.


Partial cache:

                Initially the cache is empty for every new request source query hits the database and fetches the information to the cache. For existing records, source query hits the cache.


More and more new records are added to the destination there is huge usage in existing records as well.

Look up result:

If there is no match in the look up we can go for either of the below (ways) options.

a)      Ignore failure

b)      Redirect rows to error output

c)       Fail component

d)      Redirect rows to no match O/P.

If the source is having multiple matches in the destination it returns “First Match”

Lookup performance improvement:

a)      Increase or decrease the cache memory according to the target table size, because more rows with the big size cache gives bad performance.

b)      Instead of taking table, take an SQL query to have required no.of rows and columns in the cache and to perform lookup operations.

Lookup –> rt click –> edit –> connection –> mark use results of an SQL query option and write this type of customized query.

“SELECT DEPT ID, DEPT NAME From DEPT where DEPT ID in [10,20]”

Eg: Retrieving the dept id, name from dept (using full cache) table based on the match from emp table.

Matched records in one destination and

Unmatched records to another destination.

Sol: a) OLEDB source (Emp table)

b) Lookup –> Rc –> Edit –> general select full cache


Lookup performance improvement

Specify how to handle rows with no matching entries i.e., redirect rows to no match output


OLEDB connection manager:


Use table or view:


Connect DID from EMP to DEPT and select DID, DName from Dept.


Take two destinations and connect matched result to one destination and unmatched result to another destination.


a) Unmatched result destination structure is like source table only and it contains source unmatched records.

b) Lookup operations we can perform only on relational tables (we can’t perform on flat files)

Working with partial cache:

                In the above steps do the below changes


Working with partial cache


Lookup –> Rc –> Edit –>


Select partial cache.


Take required amount of memory if required enable the cache for no matching entries options.

Note: the cache created and dropped automatically

Creating the named cache:

  1. This cache is shareable across multiple packages.
  2. This is recommendable if the lookup table data is not changed such as a long time.


Creating the named cache


Start –> programs –> open BIDS –> New –>Project –> package –>control flow task –> data flow task –> Drag or select the OLEDB source –> cache transform –> steps page no 340)

Using the preached or named cache for operations: 

To do the named cache operations, take some changes in the “lookup” i.e.,

Navigation: lookup –> rt click –> edit  –> lookup editor


Using the preached or named cache for operations

Properties –> general

Cache mode: Select full cache

Connection type: Cache connection manager

–> Specify how to handle rows with no matching entries Redirect rows to no match output.

Go to connection:

Cache connection manager –> new –> name  –>

Check use cache file name



Check columns, ok, ok

Fuzzy lookup transformation: 

This transformation is designed to get the result from the destination based on the similarity but not exact match.

While doing the operation three more columns added for destination.

a)      similarity:

It displays how much similar source row with destination row. (Column)

b)      similarity-column name:

It display how much similar each source column with each destination column.

c)       confidence:

How much the system is having confidence to give the result?

Note: generally we go for similarities for string values.



similarity-column name



EID            ENAME

1                Madhu Mohan

2                Vinay Krishna

8                Rajesh Yadav

5                Ramana

6                Murali Mohan





Fuzzy lookup:

  1. Take OLEDB source as emp.
  2. Fuzzy lookupàrt click àedità

Reference table

OLEDB connection manager: DB-MSBI

Table or view: Emp-address


Connect ename from Emp to Ename in Emp-address and select EName and E-address columns from Emp-add

Ok Ok.



4. Take two destinations and connect each condition.

5. Run package.

Pivot transformation:

  1. It converts rows information into columns.
  2. It creates less normalization of data.

Pivot transformation

Un pivoted column:

It doesn’t participate in pivoting.

Pivoted columns:

Columns values which are converted from rows to columns.

Pivoted value:  the values which are moved to pivoted columns.

Column name                                                                                 Pivoted key usage

Party ID                                                                                                1àun pivoted

SAL component                                                                                2àpivoted columns

SAL amount                                                                                     3àpivoted values

Lineage ID:

   It is a unique ID taken by a system for every column which is mapped.


Lineage ID


  1. Take pivot-SRC as source
  2. Pivot –> rt click –> edit –>

Input columns: select all columns

Input output properties:

Pivot default input:

Do the below settings

Party ID –> pivot usage –> 1

SAL component –> pivot usage –> 2

SAL amount  –> pivot usage –> 3


Identify lineage ids of party ID, sal component, sal amount


Column name                  lineage id

Party id                              349

Sal component                  371

Sal amount                         375

Pivot default O/P:

  • Click add columns –> Rename column to party ID –> Go to properties and set
  • Pivot key value: party ID, source column: 349
  • Click add columns –> Rename column to HRS –> Go to properties and set
  • Pivot key value: HRA, source column: 375
  • Click add columns –> Rename column to TA –> Go to properties and set
  • Pivot key value: TA, source column: 375
  • Click add columns –> Rename column to DA –> Go to properties and set
  • Pivot key value: DA, source column: 375


It display audit information for every row coming from source or it adds audit information to the source data.

Eg:   Audit types:

  1. Execution instance GUID
  2. Package ID
  3. Package name
  4. Version ID
  5. Execution start time
  6. m/c name
  7. User name
  8. Task name
  9. Task ID

Character map:

It applies string operations such as lower to upper and vice versa etc.

Copy column:

It creates multiple copies of the column.

Export column:

It export column value from rows in data set to a file.

Eg: exporting, image from column to a file.

Import column:

It imports in to column, values from a file

Eg: loading images from file to table rows.

Fuzzy grouping:

It groups similar rows.

OLEDB command:

It executes an SQL command for each row in a data set.

Percentage sampling:

It takes sample percentage no.of rows from source data set.

Eg: taking 20% of sample rows from a dataset.

Row sampling:

It display the specified no.of sampled rows.

Eg: display 10,000 sample rows from a table.

Row count:

It count the no.of rows in dataset.

Un pivot:

It converts columns information iTerm lookup:nto rows i.e, it creates move a normalized representation of dataset.

Script component:

It executes a customs script (VB.Net) or (C#.Net)

Term extraction:

It extracts terms from a data to column.

It counts the frequencies that terms in a reference table appears in a dataset.

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List of Related Microsoft Certification Courses:

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 SSAS SQL Server
 SharePoint BizTalk Server
 Team Foundation Server BizTalk Server Administrator


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Last updated: 03 Apr 2023
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As a content writer and storyteller, Raunaak Mitra regards himself to be a prodigy in writing. He firmly believes that language is borderless, and anyone can write as long as they have a strong narrative capability and the ability to emote feelings into words. Authors like F. Scott Fitzgerald and R. K. Narayan have influenced him to keep the writing as simple as possible for anyone to understand. Other than being well-versed about technological trends like AI, Machine Learning, AR, VR, Gaming, Microprocessors, Cloud Computing, Industry 4.0, literally any technological advancement (old or new), he also shares a knack to curate fiction on sci-fi, satire, and thriller genres.

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